Triplex
Maintainer: SciPhi
219
⚙️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
Triplex is a state-of-the-art large language model (LLM) developed by SciPhi.AI for the task of knowledge graph construction. It is a finetuned version of the Phi3-3.8B model that excels at extracting triplets - simple statements consisting of a subject, predicate, and object - from text or other data sources. Compared to GPT-4, Triplex is able to construct knowledge graphs at a 98% cost reduction while maintaining strong performance.
Unlike more expensive knowledge graph approaches like Microsoft's Graph RAG, Triplex enables local graph building at a fraction of the cost using SciPhi's R2R system. It outperforms GPT-4 on benchmark tasks related to knowledge graph construction, making it a compelling option for projects that require building knowledge graphs from unstructured data.
Model inputs and outputs
Inputs
- Unstructured text data:
Triplextakes in raw text as input and extracts knowledge graph triplets from it. - Entity types and predicates: The model also takes in a list of entity types and predicates that it should focus on when extracting triplets.
Outputs
- Knowledge graph triplets: The main output of
Triplexis a set of extracted triplets representing relationships between entities in the input text.
Capabilities
Triplex excels at the task of knowledge graph construction, outperforming GPT-4 while costing 1/60th as much. It is able to rapidly extract high-quality triplets from text, enabling users to build knowledge graphs at a fraction of the typical cost. This makes it a powerful tool for applications that require structured knowledge extracted from unstructured data sources.
What can I use it for?
Triplex is well-suited for any project that requires building knowledge graphs from text data. This could include applications in areas like:
- Business intelligence: Extracting insights and relationships from corporate documents, reports, and other internal data sources.
- Scientific research: Mapping out connections between concepts, entities, and findings in academic papers and other technical literature.
- Public sector: Aggregating and structuring information from government reports, legislation, and other public documents.
The cost-effectiveness of Triplex makes it an appealing option for organizations that need to build knowledge graphs but have limited budgets or computational resources.
Things to try
One interesting aspect of Triplex is its ability to focus on specific entity types and predicates when extracting knowledge graph triplets. This allows users to tailor the model's output to their particular needs and use cases. For example, you could experiment with different sets of entity types and predicates to see how the extracted triplets vary, and then select the configuration that is most relevant for your project.
Another thing to try is using Triplex in conjunction with SciPhi's R2R system for local knowledge graph building. By leveraging R2R, you can quickly and efficiently construct knowledge graphs from text data without the need for expensive cloud-based infrastructure.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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